## Target files
A `target.py` file describes a project and its build components.
Begin by specifying `projectRoot` which is the path from the target file to your source files. All relative paths
will be relative to this directory.
The compiler frontend informs Aim how to construct the arguments for the compiler. Use `gcc`
for GCC-like compilers and `msvc` for Microsoft cl-like compilers. Next specify the `compiler`, `archiver`, `flags` and any `defines`.
```
projectRoot = "../.."
compilerFrontend="gcc"
compiler = "clang++"
archiver = "ar"
flags = [
"-std=c++17",
"-O3",
"-g",
"-Wall",
]
# defines = [...] # Defines do not need the -D prefix.
```
Next specify your builds. For each build you must specify the `name` and `buildRule`. Valid build rules are
`staticLibrary`, `dynamicLibrary`, `executable`, `headerOnly` or `libraryReference`. A `target.py` that consists of a
dynamic or shared library, an application and a test executable looks like:
```
builds = [
{
"name": "calculatorstatic",
"buildRule": "staticLibrary",
"outputName": "CalculatorStatic",
"sourceFiles": ["lib/*.cpp"],
"includePaths": [
"include"
]
},
{
"name": "calculatordynamic",
"buildRule": "dynamicLibrary",
"outputName": "CalculatorShared",
"sourceFiles": ["lib/*.cpp"],
"includePaths": [
"include"
]
},
{
"name": "calculatortests",
"buildRule": "executable",
"requires": ["calculatorstatic"],
"outputName": "CalculatorTests",
"sourceFiles": ["tests/*.cpp"],
"includePaths": ["include"]
},
{
"name": "calculatorapp",
"buildRule": "executable",
"requires": ["calculatordynamic"],
"outputName": "CalculatorApp",
"sourceFiles": ["src/*.cpp"],
"includePaths": ["include"]
}
]
```
Other notes:
* The `requires` field is important as it is how you specify the dependencies for a build. For example, if you create a static library named "myAwesomeLibrary", this can be used in other builds simply by specifying `requires=["myAwesomeLibrary"]`.
* A `headerOnly` build does not have an `outputName` or `sourceFiles` as it is not built. The `headerOnly` rule is not essential and is mostly for convenience. If you have a header only library, repeating the include paths across several builds can be become repetitive. Instead, create a `headerOnly` build to capture the include paths and use it in other builds by adding the rule to the builds `requires` field.
* A `libraryReference` does not have `sourceFiles` as it is not built. Like the `headerOnly` rule it is mostly for convience to reduce duplication. The primary use case is for capturing the `includePaths`, `libraryPaths` and `libraries` of a third party library that you need to use in a build. A `libraryReference` can then be used by other builds by adding it to a builds `requires` field.
* The fields `compiler`, `flags` and `defines` are normally written at the top of the target file before the builds section. By default, all builds will use these fields i.e. they are global, but they can also be overridden by specifying them again in a build. Note that when these fields are specified specifically for a build, they completely replace the global definition; any `flags` or `defines` that you specify must be written out in full as they will not share any values with the global definition.
* Since target files are just python, you can have variables. However, since target files are validated with a schema, variables must be escaped with a leading underscore. For example `_custom_defines = [...]` is okay, but `custom_defines = [...]` will cause a schema error.
## Supporting Multiple Targets
Aim treats any build variation as its own unique build target with its own unique `target.py`.
A build target is some combination of _things_ that affects the output binary such as:
* operating system (Windows, OSX, Gnu Linux)
* compiler (MSVC, GCC, Clang)
* build type (Release, Debug, Sanitized)
* etc.
Each build target and corresponding `target.py` file must have its own directory ideally named using a unique
identifier that comprises the 'parts' that make up the build. For example, `builds/linux-clang++-release/target.py` indicates that the target file describes a project that is a `release` build, uses the `clang++` compiler and is for the `linux` operating system.
As an example, if you were developing an application for both Windows and Linux, you may end up with a build directory structure like the following:
* `builds/linux-clang++-release/target.py`
* `builds/linux-clang++-debug/target.py`
* `builds/windows-clangcl-release/target.py`
* `builds/windows-clangcl-debug/target.py`
Note: each `target.py` file must be written out in full for each target that you need to support. There is no way for
target files to share information or to depend on another. While this leads to duplication between target files, it
makes them very explicit and makes debugging builds much easier.
## Advice Structuring Projects
If you structure your project/libraries as individual repositories then it may seem logical to nest dependencies inside
one another. For example, if library B depends on library A, then B needs a copy of A in order for it to be built.
So you may choose to nest the source of A inside B, perhaps using a git submodule.
The problem comes when your dependency hierarchy becomes more complex. If library C also depends on A, and an application D
depends on B and C, you'll end up with multiple copies of library A which can become difficult to manage.
You may need to use this approach, as it can be useful to build a library in isolation, however you should do so in such
a way where pulling the source for the dependencies is optional.
The approach the author uses is to use a non-project-specific directory that includes all your projects directly below it
i.e. a "flat" structure. So rather than nesting dependencies you have:
```
+ MyProjects
+ - LibA
+ - LibB
+ - LibC
+ - Application_1
+ - Application_2
+ - builds
+ - - App1
+ - - - linux-clang++-debug
+ - - - - target.py
```
The flat structure has a single build directory and a single target file for each build target you need to support. This eliminates any
duplication and is easy to manage. `Aim` is flexible enough that you can add additional levels to the project structure
should you need to. For example, you may want to group all libraries under a libraries sub-directory. But the take-away message
is that you should not _enforce_ nested dependencies as this leads to duplication.
## Developing Aim
Aim is a Python project and uses the [poetry](https://python-poetry.org/) dependency manager. See [poetry installation](https://python-poetry.org/docs/#installation) for instructions.
Once you have cloned the project, the virtual environment and dependencies can be installed by executing:
```
poetry install
```
### Dev Install
Unfortunately, unlike `setuptools`, there is no means to do a 'dev install' using poetry. A dev install effectively generates
an application that internally references the active source files under development. This allows developers to test the application
without having to re-install the application after each change.
In order to use a development version of Aim on the command line, is it recommended creating an alias. The alias needs to:
* add the Aim directory to `PYTHONPATH` to resolve import/module paths
* execute the main Aim script using the virtualenv created by poetry
There are `dev-env.bash` and `dev-env.fish` scripts that configure this for you in the root of the Aim project directory.
Note, these files must be sourced in order for them to work.
%package -n python3-aim-build
Summary: A build tool for C++.
Provides: python-aim-build
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-aim-build
## Target files
A `target.py` file describes a project and its build components.
Begin by specifying `projectRoot` which is the path from the target file to your source files. All relative paths
will be relative to this directory.
The compiler frontend informs Aim how to construct the arguments for the compiler. Use `gcc`
for GCC-like compilers and `msvc` for Microsoft cl-like compilers. Next specify the `compiler`, `archiver`, `flags` and any `defines`.
```
projectRoot = "../.."
compilerFrontend="gcc"
compiler = "clang++"
archiver = "ar"
flags = [
"-std=c++17",
"-O3",
"-g",
"-Wall",
]
# defines = [...] # Defines do not need the -D prefix.
```
Next specify your builds. For each build you must specify the `name` and `buildRule`. Valid build rules are
`staticLibrary`, `dynamicLibrary`, `executable`, `headerOnly` or `libraryReference`. A `target.py` that consists of a
dynamic or shared library, an application and a test executable looks like:
```
builds = [
{
"name": "calculatorstatic",
"buildRule": "staticLibrary",
"outputName": "CalculatorStatic",
"sourceFiles": ["lib/*.cpp"],
"includePaths": [
"include"
]
},
{
"name": "calculatordynamic",
"buildRule": "dynamicLibrary",
"outputName": "CalculatorShared",
"sourceFiles": ["lib/*.cpp"],
"includePaths": [
"include"
]
},
{
"name": "calculatortests",
"buildRule": "executable",
"requires": ["calculatorstatic"],
"outputName": "CalculatorTests",
"sourceFiles": ["tests/*.cpp"],
"includePaths": ["include"]
},
{
"name": "calculatorapp",
"buildRule": "executable",
"requires": ["calculatordynamic"],
"outputName": "CalculatorApp",
"sourceFiles": ["src/*.cpp"],
"includePaths": ["include"]
}
]
```
Other notes:
* The `requires` field is important as it is how you specify the dependencies for a build. For example, if you create a static library named "myAwesomeLibrary", this can be used in other builds simply by specifying `requires=["myAwesomeLibrary"]`.
* A `headerOnly` build does not have an `outputName` or `sourceFiles` as it is not built. The `headerOnly` rule is not essential and is mostly for convenience. If you have a header only library, repeating the include paths across several builds can be become repetitive. Instead, create a `headerOnly` build to capture the include paths and use it in other builds by adding the rule to the builds `requires` field.
* A `libraryReference` does not have `sourceFiles` as it is not built. Like the `headerOnly` rule it is mostly for convience to reduce duplication. The primary use case is for capturing the `includePaths`, `libraryPaths` and `libraries` of a third party library that you need to use in a build. A `libraryReference` can then be used by other builds by adding it to a builds `requires` field.
* The fields `compiler`, `flags` and `defines` are normally written at the top of the target file before the builds section. By default, all builds will use these fields i.e. they are global, but they can also be overridden by specifying them again in a build. Note that when these fields are specified specifically for a build, they completely replace the global definition; any `flags` or `defines` that you specify must be written out in full as they will not share any values with the global definition.
* Since target files are just python, you can have variables. However, since target files are validated with a schema, variables must be escaped with a leading underscore. For example `_custom_defines = [...]` is okay, but `custom_defines = [...]` will cause a schema error.
## Supporting Multiple Targets
Aim treats any build variation as its own unique build target with its own unique `target.py`.
A build target is some combination of _things_ that affects the output binary such as:
* operating system (Windows, OSX, Gnu Linux)
* compiler (MSVC, GCC, Clang)
* build type (Release, Debug, Sanitized)
* etc.
Each build target and corresponding `target.py` file must have its own directory ideally named using a unique
identifier that comprises the 'parts' that make up the build. For example, `builds/linux-clang++-release/target.py` indicates that the target file describes a project that is a `release` build, uses the `clang++` compiler and is for the `linux` operating system.
As an example, if you were developing an application for both Windows and Linux, you may end up with a build directory structure like the following:
* `builds/linux-clang++-release/target.py`
* `builds/linux-clang++-debug/target.py`
* `builds/windows-clangcl-release/target.py`
* `builds/windows-clangcl-debug/target.py`
Note: each `target.py` file must be written out in full for each target that you need to support. There is no way for
target files to share information or to depend on another. While this leads to duplication between target files, it
makes them very explicit and makes debugging builds much easier.
## Advice Structuring Projects
If you structure your project/libraries as individual repositories then it may seem logical to nest dependencies inside
one another. For example, if library B depends on library A, then B needs a copy of A in order for it to be built.
So you may choose to nest the source of A inside B, perhaps using a git submodule.
The problem comes when your dependency hierarchy becomes more complex. If library C also depends on A, and an application D
depends on B and C, you'll end up with multiple copies of library A which can become difficult to manage.
You may need to use this approach, as it can be useful to build a library in isolation, however you should do so in such
a way where pulling the source for the dependencies is optional.
The approach the author uses is to use a non-project-specific directory that includes all your projects directly below it
i.e. a "flat" structure. So rather than nesting dependencies you have:
```
+ MyProjects
+ - LibA
+ - LibB
+ - LibC
+ - Application_1
+ - Application_2
+ - builds
+ - - App1
+ - - - linux-clang++-debug
+ - - - - target.py
```
The flat structure has a single build directory and a single target file for each build target you need to support. This eliminates any
duplication and is easy to manage. `Aim` is flexible enough that you can add additional levels to the project structure
should you need to. For example, you may want to group all libraries under a libraries sub-directory. But the take-away message
is that you should not _enforce_ nested dependencies as this leads to duplication.
## Developing Aim
Aim is a Python project and uses the [poetry](https://python-poetry.org/) dependency manager. See [poetry installation](https://python-poetry.org/docs/#installation) for instructions.
Once you have cloned the project, the virtual environment and dependencies can be installed by executing:
```
poetry install
```
### Dev Install
Unfortunately, unlike `setuptools`, there is no means to do a 'dev install' using poetry. A dev install effectively generates
an application that internally references the active source files under development. This allows developers to test the application
without having to re-install the application after each change.
In order to use a development version of Aim on the command line, is it recommended creating an alias. The alias needs to:
* add the Aim directory to `PYTHONPATH` to resolve import/module paths
* execute the main Aim script using the virtualenv created by poetry
There are `dev-env.bash` and `dev-env.fish` scripts that configure this for you in the root of the Aim project directory.
Note, these files must be sourced in order for them to work.
%package help
Summary: Development documents and examples for aim-build
Provides: python3-aim-build-doc
%description help
## Target files
A `target.py` file describes a project and its build components.
Begin by specifying `projectRoot` which is the path from the target file to your source files. All relative paths
will be relative to this directory.
The compiler frontend informs Aim how to construct the arguments for the compiler. Use `gcc`
for GCC-like compilers and `msvc` for Microsoft cl-like compilers. Next specify the `compiler`, `archiver`, `flags` and any `defines`.
```
projectRoot = "../.."
compilerFrontend="gcc"
compiler = "clang++"
archiver = "ar"
flags = [
"-std=c++17",
"-O3",
"-g",
"-Wall",
]
# defines = [...] # Defines do not need the -D prefix.
```
Next specify your builds. For each build you must specify the `name` and `buildRule`. Valid build rules are
`staticLibrary`, `dynamicLibrary`, `executable`, `headerOnly` or `libraryReference`. A `target.py` that consists of a
dynamic or shared library, an application and a test executable looks like:
```
builds = [
{
"name": "calculatorstatic",
"buildRule": "staticLibrary",
"outputName": "CalculatorStatic",
"sourceFiles": ["lib/*.cpp"],
"includePaths": [
"include"
]
},
{
"name": "calculatordynamic",
"buildRule": "dynamicLibrary",
"outputName": "CalculatorShared",
"sourceFiles": ["lib/*.cpp"],
"includePaths": [
"include"
]
},
{
"name": "calculatortests",
"buildRule": "executable",
"requires": ["calculatorstatic"],
"outputName": "CalculatorTests",
"sourceFiles": ["tests/*.cpp"],
"includePaths": ["include"]
},
{
"name": "calculatorapp",
"buildRule": "executable",
"requires": ["calculatordynamic"],
"outputName": "CalculatorApp",
"sourceFiles": ["src/*.cpp"],
"includePaths": ["include"]
}
]
```
Other notes:
* The `requires` field is important as it is how you specify the dependencies for a build. For example, if you create a static library named "myAwesomeLibrary", this can be used in other builds simply by specifying `requires=["myAwesomeLibrary"]`.
* A `headerOnly` build does not have an `outputName` or `sourceFiles` as it is not built. The `headerOnly` rule is not essential and is mostly for convenience. If you have a header only library, repeating the include paths across several builds can be become repetitive. Instead, create a `headerOnly` build to capture the include paths and use it in other builds by adding the rule to the builds `requires` field.
* A `libraryReference` does not have `sourceFiles` as it is not built. Like the `headerOnly` rule it is mostly for convience to reduce duplication. The primary use case is for capturing the `includePaths`, `libraryPaths` and `libraries` of a third party library that you need to use in a build. A `libraryReference` can then be used by other builds by adding it to a builds `requires` field.
* The fields `compiler`, `flags` and `defines` are normally written at the top of the target file before the builds section. By default, all builds will use these fields i.e. they are global, but they can also be overridden by specifying them again in a build. Note that when these fields are specified specifically for a build, they completely replace the global definition; any `flags` or `defines` that you specify must be written out in full as they will not share any values with the global definition.
* Since target files are just python, you can have variables. However, since target files are validated with a schema, variables must be escaped with a leading underscore. For example `_custom_defines = [...]` is okay, but `custom_defines = [...]` will cause a schema error.
## Supporting Multiple Targets
Aim treats any build variation as its own unique build target with its own unique `target.py`.
A build target is some combination of _things_ that affects the output binary such as:
* operating system (Windows, OSX, Gnu Linux)
* compiler (MSVC, GCC, Clang)
* build type (Release, Debug, Sanitized)
* etc.
Each build target and corresponding `target.py` file must have its own directory ideally named using a unique
identifier that comprises the 'parts' that make up the build. For example, `builds/linux-clang++-release/target.py` indicates that the target file describes a project that is a `release` build, uses the `clang++` compiler and is for the `linux` operating system.
As an example, if you were developing an application for both Windows and Linux, you may end up with a build directory structure like the following:
* `builds/linux-clang++-release/target.py`
* `builds/linux-clang++-debug/target.py`
* `builds/windows-clangcl-release/target.py`
* `builds/windows-clangcl-debug/target.py`
Note: each `target.py` file must be written out in full for each target that you need to support. There is no way for
target files to share information or to depend on another. While this leads to duplication between target files, it
makes them very explicit and makes debugging builds much easier.
## Advice Structuring Projects
If you structure your project/libraries as individual repositories then it may seem logical to nest dependencies inside
one another. For example, if library B depends on library A, then B needs a copy of A in order for it to be built.
So you may choose to nest the source of A inside B, perhaps using a git submodule.
The problem comes when your dependency hierarchy becomes more complex. If library C also depends on A, and an application D
depends on B and C, you'll end up with multiple copies of library A which can become difficult to manage.
You may need to use this approach, as it can be useful to build a library in isolation, however you should do so in such
a way where pulling the source for the dependencies is optional.
The approach the author uses is to use a non-project-specific directory that includes all your projects directly below it
i.e. a "flat" structure. So rather than nesting dependencies you have:
```
+ MyProjects
+ - LibA
+ - LibB
+ - LibC
+ - Application_1
+ - Application_2
+ - builds
+ - - App1
+ - - - linux-clang++-debug
+ - - - - target.py
```
The flat structure has a single build directory and a single target file for each build target you need to support. This eliminates any
duplication and is easy to manage. `Aim` is flexible enough that you can add additional levels to the project structure
should you need to. For example, you may want to group all libraries under a libraries sub-directory. But the take-away message
is that you should not _enforce_ nested dependencies as this leads to duplication.
## Developing Aim
Aim is a Python project and uses the [poetry](https://python-poetry.org/) dependency manager. See [poetry installation](https://python-poetry.org/docs/#installation) for instructions.
Once you have cloned the project, the virtual environment and dependencies can be installed by executing:
```
poetry install
```
### Dev Install
Unfortunately, unlike `setuptools`, there is no means to do a 'dev install' using poetry. A dev install effectively generates
an application that internally references the active source files under development. This allows developers to test the application
without having to re-install the application after each change.
In order to use a development version of Aim on the command line, is it recommended creating an alias. The alias needs to:
* add the Aim directory to `PYTHONPATH` to resolve import/module paths
* execute the main Aim script using the virtualenv created by poetry
There are `dev-env.bash` and `dev-env.fish` scripts that configure this for you in the root of the Aim project directory.
Note, these files must be sourced in order for them to work.
%prep
%autosetup -n aim-build-0.1.38
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-aim-build -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot